{"id":"https://openalex.org/W7166807663","doi":"https://doi.org/10.18653/v1/2026.acl-demo.9","title":"Fast-MIA: Efficient and Scalable Membership Inference for LLMs","display_name":"Fast-MIA: Efficient and Scalable Membership Inference for LLMs","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166807663","doi":"https://doi.org/10.18653/v1/2026.acl-demo.9"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.acl-demo.9","doi":"https://doi.org/10.18653/v1/2026.acl-demo.9","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-demo.9","pdf_url":"https://aclanthology.org/2026.acl-demo.9.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2026.acl-demo.9.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139770237","display_name":"Hiromu Takahashi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hiromu Takahashi","raw_affiliation_strings":["Independent Researcher * Tokyo , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher * Tokyo , Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034011295","display_name":"Shotaro Ishihara","orcid":"https://orcid.org/0009-0001-0366-6807","observed_orcids":["https://orcid.org/0009-0001-0366-6807"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shotaro Ishihara","raw_affiliation_strings":["Nikkei Inc. Tokyo , Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nikkei Inc. Tokyo , Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43527344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"89","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.51419997215271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.51419997215271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.46239998936653137,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/membership-inference-attacks","display_name":"membership inference attacks","score":0.9240000247955322},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9940000176429749},{"id":"https://openalex.org/keywords/privacy-auditing","display_name":"privacy auditing","score":0.7799999713897705},{"id":"https://openalex.org/keywords/copyright-infringement","display_name":"copyright infringement","score":0.7950000166893005},{"id":"https://openalex.org/keywords/batch-inference","display_name":"batch inference","score":0.5899999737739563},{"id":"https://openalex.org/keywords/vllm","display_name":"vLLM","score":0.9100000262260437},{"id":"https://openalex.org/keywords/reproducibility","display_name":"reproducibility","score":0.5260000228881836},{"id":"https://openalex.org/keywords/python-library","display_name":"Python library","score":0.42800000309944153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6062999963760376},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46860000491142273},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3792000114917755},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3398999869823456},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.24779999256134033}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.acl-demo.9","doi":"https://doi.org/10.18653/v1/2026.acl-demo.9","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-demo.9","pdf_url":"https://aclanthology.org/2026.acl-demo.9.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.acl-demo.9","doi":"https://doi.org/10.18653/v1/2026.acl-demo.9","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-demo.9","pdf_url":"https://aclanthology.org/2026.acl-demo.9.pdf","source":{"id":"https://openalex.org/S4393915740","display_name":"Proceedings of the conference - Association for Computational Linguistics. Meeting","issn_l":"0736-587X","issn":["0736-587X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166807663.pdf","grobid_xml":"https://content.openalex.org/works/W7166807663.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7166852558","https://openalex.org/W4404783485","https://openalex.org/W4411120109","https://openalex.org/W7124755153","https://openalex.org/W7125800646","https://openalex.org/W4410609172","https://openalex.org/W4414581747","https://openalex.org/W4415936085","https://openalex.org/W7212287809","https://openalex.org/W6948106365"],"abstract_inverted_index":{"We":[0],"propose":[1],"Fast-MIA":[2,73,122],"(https://github.":[3],"com/Nikkei/fast-mia),":[4],"a":[5,23,89,109],"Python":[6],"library":[7,103],"for":[8,26],"efficiently":[9],"evaluating":[10],"membership":[11],"inference":[12,80],"attacks":[13],"(MIA)":[14],"against":[15],"large":[16],"language":[17],"models":[18],"(LLMs).MIA":[19],"has":[20],"emerged":[21],"as":[22,68],"crucial":[24],"technique":[25],"auditing":[27,48],"privacy":[28],"risks":[29],"and":[30,87,98,116,131],"copyright":[31],"infringement":[32],"in":[33],"LLMs.However,":[34],"computational":[35],"demands":[36],"have":[37],"grown":[38],"substantially:":[39],"recent":[40],"methods":[41,60,107],"rely":[42],"on":[43],"repeated":[44],"inference,":[45],"while":[46],"practical":[47],"requires":[49],"large-scale":[50],"evaluation.Progress":[51],"is":[52],"further":[53],"hindered":[54],"by":[55],"existing":[56],"implementations":[57],"that":[58,93],"execute":[59],"independently,":[61],"redundantly":[62],"computing":[63],"shared":[64],"intermediate":[65,95],"results":[66,96],"such":[67],"log-probabilities.To":[69],"address":[70],"these":[71],"challenges,":[72],"combines":[74],"two":[75],"strategies:":[76],"(1)":[77],"high-throughput":[78],"batch":[79],"via":[81],"vLLM,":[82],"achieving":[83],"approximately":[84],"5\u00d7":[85],"speedup,":[86],"(2)":[88],"cross-method":[90],"caching":[91],"architecture":[92],"computes":[94],"once":[97],"shares":[99],"them":[100],"across":[101],"methods.The":[102],"includes":[104],"representative":[105],"MIA":[106,133],"under":[108,123],"unified":[110],"framework,":[111],"integrates":[112],"with":[113],"established":[114],"benchmarks,":[115],"supports":[117],"flexible":[118],"YAML":[119],"configuration.We":[120],"release":[121],"the":[124],"Apache":[125],"License":[126],"2.0":[127],"to":[128],"support":[129],"scalable":[130],"reproducible":[132],"research.":[134]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-07-02T00:00:00"}
